This is the code for the alternative statistical analysis for “Vowel Acoustics as Predictors of Speech Intelligibility in Dysarthria.”
This block of code loads in the required packages for this script. In the #’s, I have provided to the code to install each package if needed.
library(rio) # install.packages('rio')
library(tidyverse) # install.packages('tidyverse')
library(irr) # install.packages('irr')
library(performance) # install.packages('performance')
library(car) # install.packages('car')
library(ggpubr) # install.packages('ggpubr')
library(Hmisc) # install.packages('Hmisc')
library(ggridges) # install.packages('ggridges')
library(furniture) # install.packages('furniture')
library(gt) # install.packages('gt')
library(patchwork) # install.packages('patchwork')
library(ks) # install.packages('ks')
library(emuR) # install.packages('emuR')
# Reliability Data
Reliability <- rio::import("Prepped Data/Reliability Data.csv")
# Speaker Data
AcousticData <- rio::import("Prepped Data/AcousticMeasures.csv") %>%
dplyr::filter(!grepl("_rel", Speaker)) %>% # Filters out reliability data
dplyr::select(c(Speaker,
Sex,
Etiology,
vowel_ED_b, # Corner Dispersion
VSA_b, # Traditional VSA
Hull_b, # VSA Hull
Hull_bVSD_25, # VSD 25
Hull_bVSD_75, # VSD 75
VAS, # Intelligibility (VAS)
transAcc) # Intelligibility (OT)
) %>%
# The following code ensure etiology, sex, and speaker are coded as factors
dplyr::mutate(Etiology = as.factor(Etiology),
Sex = as.factor(Sex),
Speaker = as.factor(Speaker))
# Listener Data
Listeners <- rio::import("Prepped Data/Listener_Demographics.csv") %>%
dplyr::select(!c(StartDate:proloficID, # removes unwanted columns
Q2.4_6_TEXT,
Q3.2_8_TEXT,
AudioCheck:EP3)) %>%
# The follow code corrects for when a listener replied "Other" instead of the Biracial or Multiracial" response
dplyr::mutate(race = case_when(
Q3.3_7_TEXT == "Native American/ African amercing" ~ "Biracial or Multiracial",
TRUE ~ race
))
In this alternate analysis, we are looking at the relationship between these acoustic measures with speech intelligibility for the ALS/PD and the HD/Ataxic speakers separately. We create a new variable called Incoord, where the ALS/PD Speakers are set as the reference group (in order to compare to the Ataxic/HD Speaker Group). Group Comparisons, additional data visualizations, and further linear model comparisons are completed.
This block creates metagroups comprising of Group A (ALS & PD), and Group B (HD & Ataxia). These two
AcousticData <- AcousticData %>%
dplyr::mutate(metaGroup = case_when(Etiology == "HD" ~ "A",
Etiology == "Ataxic" ~ "A",
TRUE ~ "B")) %>%
dplyr::mutate(metaGroup = as.factor(metaGroup))
## Specify the Model
VSA_group <- aov(VSA_b ~ Etiology, data = AcousticData)
## Assumption Check
plot(VSA_group, 1)
plot(VSA_group, 2)
car::leveneTest(VSA_group)
VSA_residuals <- residuals(object = VSA_group)
shapiro.test(VSA_residuals)
## Model Results
summary(VSA_group)
## Kruskal-Wallis Test
kruskal.test(VSA_b ~ Etiology, data = AcousticData)
## Pairwise Comparisons
pairwise.wilcox.test(AcousticData$VSA_b, AcousticData$Etiology, p.adjust.method = "bonferroni")
## Specify the Model
disp_group <- aov(vowel_ED_b ~ Etiology, data = AcousticData)
## Assumption Check
plot(disp_group, 1)
plot(disp_group, 2)
car::leveneTest(disp_group)
disp_residuals <- residuals(object = disp_group)
shapiro.test(disp_residuals)
## Model Results
summary(disp_group)
## Specify the Model
hull_group <- aov(Hull_b ~ Etiology, data = AcousticData)
## Assumption Check
plot(hull_group, 1)
plot(hull_group, 2)
car::leveneTest(hull_group)
hull_residuals <- residuals(object = hull_group)
shapiro.test(hull_residuals)
## Model Results
summary(hull_group)
## Specify the Model
vsd25_group <- aov(Hull_bVSD_25 ~ Etiology, data = AcousticData)
## Assumption Check
plot(vsd25_group, 1)
plot(vsd25_group, 2)
car::leveneTest(vsd25_group)
vsd25_residuals <- residuals(object = vsd25_group)
shapiro.test(vsd25_residuals)
## Model Summary
summary(vsd25_group)
## Specify the Model
vsd75_group <- aov(Hull_bVSD_75 ~ Etiology, data = AcousticData)
## Assumption Check
plot(vsd75_group, 1)
plot(vsd75_group, 2)
car::leveneTest(vsd75_group)
vsd75_residuals <- residuals(object = vsd75_group)
shapiro.test(vsd75_residuals)
## Model Summary
summary(vsd75_group)
## Kruskal Wallis
kruskal.test(Hull_bVSD_75 ~ Etiology, data = AcousticData)
This is the analysis for the two metagroups, Group A and B.
groupA <- AcousticData %>%
dplyr::filter(metaGroup == "A")
groupB <- AcousticData %>%
dplyr::filter(metaGroup == "B")
VSA_b_t
Two Sample t-test
data: groupA$VSA_b and groupB$VSA_b
t = 2.8889, df = 38, p-value = 0.006352
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.519310 2.951478
sample estimates:
mean of x mean of y
5.289125 3.553732
disp_t
Two Sample t-test
data: groupA$vowel_ED_b and groupB$vowel_ED_b
t = 2.031, df = 38, p-value = 0.04929
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.000790061 0.485404644
sample estimates:
mean of x mean of y
2.165220 1.922123
hull_t
Two Sample t-test
data: groupA$Hull_b and groupB$Hull_b
t = 2.4391, df = 38, p-value = 0.0195
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
1.084279 11.670189
sample estimates:
mean of x mean of y
34.14243 27.76520
# Assumption Check
## Checking Normality
with(AcousticData, shapiro.test(Hull_bVSD_25[metaGroup == "A"]))
Shapiro-Wilk normality test
data: Hull_bVSD_25[metaGroup == "A"]
W = 0.966, p-value = 0.6692
with(AcousticData, shapiro.test(Hull_bVSD_25[metaGroup == "B"]))
Shapiro-Wilk normality test
data: Hull_bVSD_25[metaGroup == "B"]
W = 0.93529, p-value = 0.1951
## Equal Variance Check
res.ftest.vsd25 <- var.test(Hull_bVSD_25 ~ metaGroup, data = AcousticData)
res.ftest.vsd25
F test to compare two variances
data: Hull_bVSD_25 by metaGroup
F = 0.80673, num df = 19, denom df = 19, p-value = 0.6444
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.3193148 2.0381717
sample estimates:
ratio of variances
0.8067331
# Model Results
vsd25_t <- t.test(groupA$Hull_bVSD_25, groupB$Hull_bVSD_25, var.equal = T)
vsd25_t
Two Sample t-test
data: groupA$Hull_bVSD_25 and groupB$Hull_bVSD_25
t = 1.4368, df = 38, p-value = 0.159
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.234943 7.274708
sample estimates:
mean of x mean of y
17.36480 14.34491
vsd75_MW
Wilcoxon rank sum exact test
data: Hull_bVSD_75 by metaGroup
W = 240, p-value = 0.2888
alternative hypothesis: true location shift is not equal to 0
# Assumption Check
## Checking Normality
with(AcousticData, shapiro.test(transAcc[metaGroup == "A"]))
Shapiro-Wilk normality test
data: transAcc[metaGroup == "A"]
W = 0.91581, p-value = 0.0823
with(AcousticData, shapiro.test(transAcc[metaGroup == "B"]))
Shapiro-Wilk normality test
data: transAcc[metaGroup == "B"]
W = 0.87029, p-value = 0.01189
## Equal Variance Check
res.ftest.OT <- var.test(transAcc ~ metaGroup, data = AcousticData)
res.ftest.OT
F test to compare two variances
data: transAcc by metaGroup
F = 0.92421, num df = 19, denom df = 19, p-value = 0.8654
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.3658124 2.3349640
sample estimates:
ratio of variances
0.9242071
# Model Results (Mann-Whitney U test conducted since assumption of normality is violated)
OT_MW <- wilcox.test(transAcc ~ metaGroup, data = AcousticData)
OT_MW
Wilcoxon rank sum exact test
data: transAcc by metaGroup
W = 206, p-value = 0.8831
alternative hypothesis: true location shift is not equal to 0
# Assumption Check
## Checking Normality
with(AcousticData, shapiro.test(VAS[metaGroup == "A"]))
Shapiro-Wilk normality test
data: VAS[metaGroup == "A"]
W = 0.91494, p-value = 0.07923
with(AcousticData, shapiro.test(VAS[metaGroup == "B"]))
Shapiro-Wilk normality test
data: VAS[metaGroup == "B"]
W = 0.87506, p-value = 0.01444
## Equal Variance Check
res.ftest.VAS <- var.test(VAS ~ metaGroup, data = AcousticData)
res.ftest.VAS
F test to compare two variances
data: VAS by metaGroup
F = 0.93077, num df = 19, denom df = 19, p-value = 0.8774
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.3684087 2.3515361
sample estimates:
ratio of variances
0.9307666
# Model Results (Mann-Whitney U test conducted since assumption of normality is violated)
OT_MW <- wilcox.test(VAS ~ metaGroup, data = AcousticData)
OT_MW
Wilcoxon rank sum exact test
data: VAS by metaGroup
W = 193, p-value = 0.862
alternative hypothesis: true location shift is not equal to 0
Since we found significant group differences for some acoustic measures between the ALS/PD and Ataxic/HD groups, we continued the heirarichal regression approach from OT Model 5. Adding in the Incoord predictor along with the interactions between the acoustic measures did not significantly improve model fit. So our original final OT model is retained.
## Specifying Model 6
OT_Model6 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b + metaGroup, data = AcousticData)
## Model 6 Assumption Check
performance::check_model(OT_Model6)
## Model 6 Summary
summary(OT_Model6)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b + vowel_ED_b + metaGroup, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-42.105 -11.266 4.121 13.717 31.536
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.7252 22.9429 0.206 0.8381
Hull_bVSD_25 -1.6809 1.1892 -1.413 0.1669
Hull_bVSD_75 0.9833 2.1390 0.460 0.6487
Hull_b 1.0629 0.7946 1.338 0.1901
VSA_b 6.5678 2.6475 2.481 0.0184 *
vowel_ED_b 4.8102 12.8358 0.375 0.7102
metaGroupB 12.8123 7.6496 1.675 0.1034
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.02 on 33 degrees of freedom
Multiple R-squared: 0.346, Adjusted R-squared: 0.2271
F-statistic: 2.91 on 6 and 33 DF, p-value: 0.02173
## Model 5 and Model 6 Comparison
anova(OT_Model5, OT_Model6)
Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_75 + VSA_b + vowel_ED_b
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup
Res.Df RSS Df Sum of Sq F Pr(>F)
1 36 16349
2 33 14583 3 1766 1.3321 0.2806
## Specifying Model 6
OT_Model6 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b + metaGroup, data = AcousticData)
## Specifying Model 7
OT_Model7 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup*Hull_bVSD_25, data = AcousticData)
## Model 7 Assumption Check
performance::check_model(OT_Model7)
## Model 7 Summary
summary(OT_Model7)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b + vowel_ED_b + metaGroup + metaGroup * Hull_bVSD_25,
data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-41.665 -10.983 4.212 14.044 31.363
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.6687 25.0611 0.106 0.9159
Hull_bVSD_25 -1.5448 1.3541 -1.141 0.2624
Hull_bVSD_75 0.8920 2.2093 0.404 0.6891
Hull_b 1.0871 0.8136 1.336 0.1909
VSA_b 6.5883 2.6881 2.451 0.0199 *
vowel_ED_b 4.3440 13.1935 0.329 0.7441
metaGroupB 16.7140 19.2373 0.869 0.3914
Hull_bVSD_25:metaGroupB -0.2417 1.0905 -0.222 0.8260
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.33 on 32 degrees of freedom
Multiple R-squared: 0.347, Adjusted R-squared: 0.2042
F-statistic: 2.429 on 7 and 32 DF, p-value: 0.04086
## Model 6 and Model 7 Comparison
anova(OT_Model6, OT_Model7)
Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25
Res.Df RSS Df Sum of Sq F Pr(>F)
1 33 14583
2 32 14561 1 22.358 0.0491 0.826
## Specifying Model 8
OT_Model8 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup*Hull_bVSD_25 + metaGroup*Hull_bVSD_75, data = AcousticData)
## Model 8 Assumption Check
performance::check_model(OT_Model8)
## Model 8 Summary
summary(OT_Model8)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b + vowel_ED_b + metaGroup + metaGroup * Hull_bVSD_25 +
metaGroup * Hull_bVSD_75, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-41.858 -12.366 6.261 13.135 30.401
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.1761 26.4727 0.498 0.6222
Hull_bVSD_25 -2.5510 1.5958 -1.599 0.1201
Hull_bVSD_75 4.6567 3.8865 1.198 0.2399
Hull_b 0.9879 0.8132 1.215 0.2336
VSA_b 6.8700 2.6831 2.560 0.0155 *
vowel_ED_b 4.0042 13.1193 0.305 0.7622
metaGroupB 6.3149 21.0758 0.300 0.7665
Hull_bVSD_25:metaGroupB 1.1508 1.6068 0.716 0.4792
Hull_bVSD_75:metaGroupB -5.4156 4.6125 -1.174 0.2493
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.21 on 31 degrees of freedom
Multiple R-squared: 0.3748, Adjusted R-squared: 0.2135
F-statistic: 2.323 on 8 and 31 DF, p-value: 0.04412
## Model 7 and Model 8 Comparison
anova(OT_Model7, OT_Model8)
Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75
Res.Df RSS Df Sum of Sq F Pr(>F)
1 32 14561
2 31 13941 1 619.94 1.3786 0.2493
## Specifying Model 9
OT_Model9 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b + metaGroup +
metaGroup*Hull_bVSD_25 +
metaGroup*Hull_bVSD_75 +
metaGroup*Hull_b, data = AcousticData)
## Model 9 Assumption Check
performance::check_model(OT_Model9)
## Model 9 Summary
summary(OT_Model9)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b + vowel_ED_b + metaGroup + metaGroup * Hull_bVSD_25 +
metaGroup * Hull_bVSD_75 + metaGroup * Hull_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-43.387 -11.747 5.389 14.794 30.552
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1279 33.4230 -0.004 0.9970
Hull_bVSD_25 -3.2653 1.9381 -1.685 0.1024
Hull_bVSD_75 4.9580 3.9484 1.256 0.2189
Hull_b 1.6193 1.2577 1.287 0.2078
VSA_b 6.9293 2.7092 2.558 0.0158 *
vowel_ED_b 5.4227 13.4118 0.404 0.6888
metaGroupB 22.3892 32.2682 0.694 0.4931
Hull_bVSD_25:metaGroupB 2.4414 2.5348 0.963 0.3432
Hull_bVSD_75:metaGroupB -6.0684 4.7580 -1.275 0.2120
Hull_b:metaGroupB -1.1098 1.6753 -0.662 0.5128
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.4 on 30 degrees of freedom
Multiple R-squared: 0.3838, Adjusted R-squared: 0.199
F-statistic: 2.076 on 9 and 30 DF, p-value: 0.06468
## Model 8 and Model 9 Comparison
anova(OT_Model8, OT_Model9)
Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75 +
metaGroup * Hull_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 31 13941
2 30 13740 1 200.96 0.4388 0.5128
## Specifying Model 10
OT_Model10 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup +
metaGroup*Hull_bVSD_25 +
metaGroup*Hull_bVSD_75 +
metaGroup*Hull_b +
metaGroup*VSA_b,
data = AcousticData)
## Model 10 Assumption Check
performance::check_model(OT_Model10)
## Model 10 Summary
summary(OT_Model10)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b + vowel_ED_b + metaGroup + metaGroup * Hull_bVSD_25 +
metaGroup * Hull_bVSD_75 + metaGroup * Hull_b + metaGroup *
VSA_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-43.360 -12.039 5.446 15.103 30.988
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7791 34.9494 0.022 0.9824
Hull_bVSD_25 -3.2153 2.0215 -1.591 0.1226
Hull_bVSD_75 4.9024 4.0460 1.212 0.2354
Hull_b 1.6152 1.2795 1.262 0.2169
VSA_b 6.7847 3.0459 2.227 0.0338 *
vowel_ED_b 5.0854 13.9703 0.364 0.7185
metaGroupB 21.7310 33.3409 0.652 0.5197
Hull_bVSD_25:metaGroupB 2.3862 2.6248 0.909 0.3708
Hull_bVSD_75:metaGroupB -6.0316 4.8495 -1.244 0.2236
Hull_b:metaGroupB -1.1357 1.7194 -0.660 0.5141
VSA_b:metaGroupB 0.5224 4.6911 0.111 0.9121
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.76 on 29 degrees of freedom
Multiple R-squared: 0.3841, Adjusted R-squared: 0.1717
F-statistic: 1.809 on 10 and 29 DF, p-value: 0.1038
## Model 9 and Model 10 Comparison
anova(OT_Model9, OT_Model10)
Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75 +
metaGroup * Hull_b
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75 +
metaGroup * Hull_b + metaGroup * VSA_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 30 13740
2 29 13734 1 5.8732 0.0124 0.9121
## Specifying Model 11
OT_Model11 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup +
metaGroup*Hull_bVSD_25 +
metaGroup*Hull_bVSD_75 +
metaGroup*Hull_b +
metaGroup*VSA_b +
metaGroup*vowel_ED_b,
data = AcousticData)
## Model 11 Assumption Check
performance::check_model(OT_Model11)
## Model 11 Summary
summary(OT_Model11)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b + vowel_ED_b + metaGroup + metaGroup * Hull_bVSD_25 +
metaGroup * Hull_bVSD_75 + metaGroup * Hull_b + metaGroup *
VSA_b + metaGroup * vowel_ED_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-43.580 -9.987 5.535 12.978 34.285
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -22.3808 39.7206 -0.563 0.5776
Hull_bVSD_25 -3.6045 2.0327 -1.773 0.0871 .
Hull_bVSD_75 5.4654 4.0435 1.352 0.1873
Hull_b 1.6498 1.2703 1.299 0.2046
VSA_b 5.1948 3.3022 1.573 0.1269
vowel_ED_b 21.5790 19.5475 1.104 0.2790
metaGroupB 65.5861 49.3675 1.329 0.1947
Hull_bVSD_25:metaGroupB 2.4987 2.6071 0.958 0.3460
Hull_bVSD_75:metaGroupB -7.0129 4.8830 -1.436 0.1620
Hull_b:metaGroupB -0.9481 1.7139 -0.553 0.5845
VSA_b:metaGroupB 4.9394 5.9410 0.831 0.4128
vowel_ED_b:metaGroupB -33.2037 27.7349 -1.197 0.2413
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.6 on 28 degrees of freedom
Multiple R-squared: 0.4141, Adjusted R-squared: 0.1839
F-statistic: 1.799 on 11 and 28 DF, p-value: 0.1025
## Model 10 and Model 11 Comparison
anova(OT_Model10, OT_Model11)
Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75 +
metaGroup * Hull_b + metaGroup * VSA_b
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75 +
metaGroup * Hull_b + metaGroup * VSA_b + metaGroup * vowel_ED_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 29 13734
2 28 13065 1 668.77 1.4332 0.2413
Since we found significant group differences for some acoustic measures between the ALS/PD and Ataxic/HD groups, we continued the hierarchical regression approach from VAS Model 5. VAS Model 6 fit significantly better than VAS Model 5. However, adding in the interactions between Incoord and the acoustic measures did not significantly improve model fit.
## Specifying Model 6
VAS_Model6 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup, data = AcousticData)
## Model 6 Assumption Check
performance::check_model(VAS_Model6)
## Model 6 Summary
summary(VAS_Model6)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b +
vowel_ED_b + metaGroup, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-41.188 -13.751 2.167 16.256 36.926
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.2778 24.8693 -0.373 0.7115
Hull_bVSD_25 -2.1615 1.2891 -1.677 0.1030
Hull_bVSD_75 1.7100 2.3186 0.738 0.4660
Hull_b 1.2359 0.8613 1.435 0.1607
VSA_b 7.4668 2.8698 2.602 0.0138 *
vowel_ED_b 5.5718 13.9135 0.400 0.6914
metaGroupB 17.9628 8.2919 2.166 0.0376 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 22.79 on 33 degrees of freedom
Multiple R-squared: 0.3632, Adjusted R-squared: 0.2474
F-statistic: 3.137 on 6 and 33 DF, p-value: 0.0152
## Specifying Model 7
VAS_Model7 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup*Hull_bVSD_25, data = AcousticData)
## Model 7 Assumption Check
performance::check_model(VAS_Model7)
## Model 7 Summary
summary(VAS_Model7)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b +
vowel_ED_b + metaGroup + metaGroup * Hull_bVSD_25, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-42.039 -14.972 3.129 15.900 35.174
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -14.8027 27.0579 -0.547 0.5881
Hull_bVSD_25 -1.7957 1.4620 -1.228 0.2283
Hull_bVSD_75 1.4646 2.3853 0.614 0.5435
Hull_b 1.3009 0.8784 1.481 0.1484
VSA_b 7.5218 2.9023 2.592 0.0143 *
vowel_ED_b 4.3193 14.2447 0.303 0.7637
metaGroupB 28.4451 20.7701 1.370 0.1804
Hull_bVSD_25:metaGroupB -0.6494 1.1773 -0.552 0.5851
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.03 on 32 degrees of freedom
Multiple R-squared: 0.3692, Adjusted R-squared: 0.2312
F-statistic: 2.675 on 7 and 32 DF, p-value: 0.02675
## Model 6 and Model 7 Comparison
anova(VAS_Model6, VAS_Model7)
Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25
Res.Df RSS Df Sum of Sq F Pr(>F)
1 33 17135
2 32 16973 1 161.37 0.3042 0.5851
## Specifying Model 8
VAS_Model8 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup*Hull_bVSD_25 + metaGroup*Hull_bVSD_75, data = AcousticData)
## Model 8 Assumption Check
performance::check_model(VAS_Model8)
## Model 8 Summary
summary(VAS_Model8)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b +
vowel_ED_b + metaGroup + metaGroup * Hull_bVSD_25 + metaGroup *
Hull_bVSD_75, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-42.267 -15.867 1.797 15.875 35.281
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.9398 28.9821 -0.274 0.7859
Hull_bVSD_25 -2.4529 1.7471 -1.404 0.1703
Hull_bVSD_75 3.9235 4.2549 0.922 0.3636
Hull_b 1.2361 0.8903 1.388 0.1749
VSA_b 7.7058 2.9374 2.623 0.0134 *
vowel_ED_b 4.0973 14.3629 0.285 0.7773
metaGroupB 21.6529 23.0737 0.938 0.3553
Hull_bVSD_25:metaGroupB 0.2601 1.7591 0.148 0.8834
Hull_bVSD_75:metaGroupB -3.5372 5.0497 -0.700 0.4889
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.22 on 31 degrees of freedom
Multiple R-squared: 0.379, Adjusted R-squared: 0.2188
F-statistic: 2.365 on 8 and 31 DF, p-value: 0.04086
## Model 7 and Model 8 Comparison
anova(VAS_Model7, VAS_Model8)
Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75
Res.Df RSS Df Sum of Sq F Pr(>F)
1 32 16973
2 31 16709 1 264.47 0.4907 0.4889
## Specifying Model 9
VAS_Model9 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup +
metaGroup*Hull_bVSD_25 +
metaGroup*Hull_bVSD_75 +
metaGroup*Hull_b,
data = AcousticData)
## Model 9 Assumption Check
performance::check_model(VAS_Model9)
## Model 9 Summary
summary(VAS_Model9)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b +
vowel_ED_b + metaGroup + metaGroup * Hull_bVSD_25 + metaGroup *
Hull_bVSD_75 + metaGroup * Hull_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-41.224 -14.209 4.443 15.797 35.400
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -18.442 36.719 -0.502 0.6192
Hull_bVSD_25 -3.017 2.129 -1.417 0.1668
Hull_bVSD_75 4.161 4.338 0.959 0.3451
Hull_b 1.734 1.382 1.255 0.2191
VSA_b 7.753 2.976 2.605 0.0142 *
vowel_ED_b 5.217 14.735 0.354 0.7258
metaGroupB 34.342 35.451 0.969 0.3404
Hull_bVSD_25:metaGroupB 1.279 VAS 0.459 0.6494
Hull_bVSD_75:metaGroupB -4.053 5.227 -0.775 0.4443
Hull_b:metaGroupB -0.876 1.841 -0.476 0.6376
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.51 on 30 degrees of freedom
Multiple R-squared: 0.3837, Adjusted R-squared: 0.1988
F-statistic: 2.075 on 9 and 30 DF, p-value: 0.06485
## Model 8 and Model 9 Comparison
anova(VAS_Model8, VAS_Model9)
Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75 +
metaGroup * Hull_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 31 16709
2 30 16584 1 125.23 0.2265 0.6376
## Specifying Model 10
VAS_Model10 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup +
metaGroup*Hull_bVSD_25 +
metaGroup*Hull_bVSD_75 +
metaGroup*Hull_b +
metaGroup*VSA_b,
data = AcousticData)
## Model 10 Assumption Check
performance::check_model(VAS_Model10)
## Model 10 Summary
summary(VAS_Model10)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b +
vowel_ED_b + metaGroup + metaGroup * Hull_bVSD_25 + metaGroup *
Hull_bVSD_75 + metaGroup * Hull_b + metaGroup * VSA_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-41.129 -14.693 2.991 15.841 36.960
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -15.1947 38.3175 -0.397 0.6946
Hull_bVSD_25 -2.8375 2.2163 -1.280 0.2106
Hull_bVSD_75 3.9624 4.4359 0.893 0.3791
Hull_b 1.7199 1.4028 1.226 0.2300
VSA_b 7.2347 3.3394 2.166 0.0386 *
vowel_ED_b 4.0097 15.3166 0.262 0.7953
metaGroupB 31.9854 36.5539 0.875 0.3888
Hull_bVSD_25:metaGroupB 1.0815 2.8777 0.376 0.7098
Hull_bVSD_75:metaGroupB -3.9209 5.3169 -0.737 0.4668
Hull_b:metaGroupB -0.9688 1.8851 -0.514 0.6112
VSA_b:metaGroupB 1.8702 5.1432 0.364 0.7188
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.86 on 29 degrees of freedom
Multiple R-squared: 0.3865, Adjusted R-squared: 0.1749
F-statistic: 1.827 on 10 and 29 DF, p-value: 0.1001
## Model 9 and Model 10 Comparison
anova(VAS_Model9, VAS_Model10)
Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75 +
metaGroup * Hull_b
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75 +
metaGroup * Hull_b + metaGroup * VSA_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 30 16584
2 29 16508 1 75.269 0.1322 0.7188
## Specifying Model 11
VAS_Model11 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup +
metaGroup*Hull_bVSD_25 +
metaGroup*Hull_bVSD_75 +
metaGroup*Hull_b +
metaGroup*VSA_b +
metaGroup*vowel_ED_b,
data = AcousticData)
## Model 11 Assumption Check
performance::check_model(VAS_Model11)
## Model 11 Summary
summary(VAS_Model11)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b +
vowel_ED_b + metaGroup + metaGroup * Hull_bVSD_25 + metaGroup *
Hull_bVSD_75 + metaGroup * Hull_b + metaGroup * VSA_b + metaGroup *
vowel_ED_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-40.891 -10.110 1.062 12.791 41.858
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -49.6015 42.6063 -1.164 0.2542
Hull_bVSD_25 -3.4158 2.1804 -1.567 0.1284
Hull_bVSD_75 4.7987 4.3373 1.106 0.2780
Hull_b 1.7713 1.3626 1.300 0.2042
VSA_b 4.8728 3.5421 1.376 0.1798
vowel_ED_b 28.5129 20.9676 1.360 0.1847
metaGroupB 97.1376 52.9541 1.834 0.0772 .
Hull_bVSD_25:metaGroupB 1.2486 2.7965 0.447 0.6587
Hull_bVSD_75:metaGroupB -5.3787 5.2378 -1.027 0.3133
Hull_b:metaGroupB -0.6902 1.8384 -0.375 0.7102
VSA_b:metaGroupB 8.4322 6.3726 1.323 0.1965
vowel_ED_b:metaGroupB -49.3281 29.7498 -1.658 0.1085
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.17 on 28 degrees of freedom
Multiple R-squared: 0.4413, Adjusted R-squared: 0.2218
F-statistic: 2.011 on 11 and 28 DF, p-value: 0.06657
## Model 10 and Model 11 Comparison
anova(VAS_Model10, VAS_Model11)
Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75 +
metaGroup * Hull_b + metaGroup * VSA_b
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
metaGroup + metaGroup * Hull_bVSD_25 + metaGroup * Hull_bVSD_75 +
metaGroup * Hull_b + metaGroup * VSA_b + metaGroup * vowel_ED_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 29 16508
2 28 15032 1 1476 2.7493 0.1085
Since VAS Model 6 was significantly better fit than Model 5, we fit a new final parsimonious model for VAS to the data (VAS ~ VSA_b + metaGroup) and compared that to the old final VAS model (VAS ~ VSA_b). The new final model was not a significantly better fit than the old final model. Thus the old final model (VAS ~ VSA_b) is retained.
## Comparison to Old Final Model
anova(VAS_Model_final, VAS_Model_newfinal)
Analysis of Variance Table
Model 1: VAS ~ VSA_b
Model 2: VAS ~ VSA_b + metaGroup
Res.Df RSS Df Sum of Sq F Pr(>F)
1 38 20556
2 37 18802 1 1753.6 3.4509 0.07119 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Because some correlations between predictors are high. We run a series of simple linear regressions to test if the predictors significantly predict each intelligibility measure on their own (VSD 25 and VSA models were already complete in our initial model comparison approach)
# OT ~ VSD 75
OT_vsd75_model <- lm(transAcc ~ Hull_bVSD_75, data = AcousticData)
performance::check_model(OT_vsd75_model)
summary(OT_vsd75_model)
Call:
lm(formula = transAcc ~ Hull_bVSD_75, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-47.60 -16.32 6.10 16.13 31.96
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 56.4278 5.4314 10.389 1.17e-12 ***
Hull_bVSD_75 0.9052 1.7124 0.529 0.6
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 24.14 on 38 degrees of freedom
Multiple R-squared: 0.0073, Adjusted R-squared: -0.01882
F-statistic: 0.2794 on 1 and 38 DF, p-value: 0.6001
# OT ~ Hull
OT_hull_model <- lm(transAcc ~ Hull_b, data = AcousticData)
performance::check_model(OT_hull_model)
summary(OT_hull_model)
Call:
lm(formula = transAcc ~ Hull_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-53.141 -12.306 4.848 18.324 31.854
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.0817 13.5989 2.506 0.0166 *
Hull_b 0.7879 0.4231 1.862 0.0703 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.19 on 38 degrees of freedom
Multiple R-squared: 0.08365, Adjusted R-squared: 0.05953
F-statistic: 3.469 on 1 and 38 DF, p-value: 0.07029
# OT ~ Corner Dispersion
OT_disp_model <- lm(transAcc ~ vowel_ED_b, data = AcousticData)
performance::check_model(OT_disp_model)
summary(OT_disp_model)
Call:
lm(formula = transAcc ~ vowel_ED_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-53.949 -10.348 4.268 14.982 25.903
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.227 18.611 0.335 0.73977
vowel_ED_b 25.564 8.946 2.857 0.00689 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.98 on 38 degrees of freedom
Multiple R-squared: 0.1769, Adjusted R-squared: 0.1552
F-statistic: 8.165 on 1 and 38 DF, p-value: 0.006891
# VAS ~ VSD 75
VAS_vsd75_model <- lm(VAS ~ Hull_bVSD_75, data = AcousticData)
performance::check_model(VAS_vsd75_model)
summary(VAS_vsd75_model)
Call:
lm(formula = VAS ~ Hull_bVSD_75, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-47.036 -18.277 9.543 21.192 37.312
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.543 5.963 8.308 4.51e-10 ***
Hull_bVSD_75 1.067 1.880 0.567 0.574
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 26.5 on 38 degrees of freedom
Multiple R-squared: 0.0084, Adjusted R-squared: -0.01769
F-statistic: 0.3219 on 1 and 38 DF, p-value: 0.5738
# VAS ~ Hull
VAS_hull_model <- lm(VAS ~ Hull_b, data = AcousticData)
performance::check_model(VAS_hull_model)
summary(VAS_hull_model)
Call:
lm(formula = VAS ~ Hull_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-52.65 -19.68 7.88 21.88 33.56
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.3768 15.0913 1.880 0.0677 .
Hull_b 0.7616 0.4695 1.622 0.1130
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 25.73 on 38 degrees of freedom
Multiple R-squared: 0.06476, Adjusted R-squared: 0.04015
F-statistic: 2.631 on 1 and 38 DF, p-value: 0.113
# VAS ~ Corner Dispersion
VAS_disp_model <- lm(VAS ~ vowel_ED_b, data = AcousticData)
performance::check_model(VAS_disp_model)
summary(VAS_disp_model)
Call:
lm(formula = VAS ~ vowel_ED_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-52.146 -13.413 7.142 18.719 32.964
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.859 20.636 -0.139 0.8905
vowel_ED_b 26.819 9.920 2.704 0.0102 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 24.37 on 38 degrees of freedom
Multiple R-squared: 0.1613, Adjusted R-squared: 0.1393
F-statistic: 7.31 on 1 and 38 DF, p-value: 0.0102